Climate variability and conflict risk in East Africa, 1990-2009

Abstract

Recent studies concerning the possible relationship between climate trends and the risks of violent conflict have yielded contradictory results, partly because of choices of conflict measures and modeling design. In this study, we examine climate-conflict relationships using a geographically disaggregated approach. We consider the effects of climate change to be both local and national in character, and we use a conflict database that contains 16,359 individual geolocated violent events for East Africa from 1990 to 2009. Unlike previous studies that relied exclusively on political and economic controls, we analyze the many geographical factors that have been shown to be important in understanding the distribution and causes of violence while also considering yearly and country fixed effects. For our main climate indicators at gridded 1° resolution (~100 km), wetter deviations from the precipitation norms decrease the risk of violence, whereas drier and normal periods show no effects. The relationship between temperature and conflict shows that much warmer than normal temperatures raise the risk of violence, whereas average and cooler temperatures have no effect. These precipitation and temperature effects are statistically significant but have modest influence in terms of predictive power in a model with political, economic, and physical geographic predictors. Large variations in the climate-conflict relationships are evident between the nine countries of the study region and across time periods.

Fig. 1.

These plots show the coefficient estimate and 95% confidence interval over the range of SPI6 (A) and TI6 (B) for the model in Table 1, column f. Nonoverlap between the confidence interval and dashed zero line indicates a statistically significant effect. The lower dark gray plots show the density distributions of the variable—both SPI6 and TI6 are centered right of zero, indicating that our study period is wetter and warmer than the 60-y comparison period.

Fig. 2.

Change in predictive power vs. statistical significance for the model in Table 1, column f. The positions of the predictors on the graph clearly indicate the modest contribution of the climate predictors to the model. Geographic variables (cell populations, space–time clustering effect, capital city locations, distance to international borders, grassland ratio, and distance to road) are more important in predictive power than the climate or political measures.